Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations1281
Missing cells0
Missing cells (%)0.0%
Duplicate rows43
Duplicate rows (%)3.4%
Total size in memory150.2 KiB
Average record size in memory120.1 B

Variable types

Numeric14
Categorical1

Alerts

Dataset has 43 (3.4%) duplicate rowsDuplicates
HCT is highly overall correlated with MCH and 2 other fieldsHigh correlation
HGB is highly overall correlated with MCH and 3 other fieldsHigh correlation
MCH is highly overall correlated with HCT and 3 other fieldsHigh correlation
MCHC is highly overall correlated with HGB and 1 other fieldsHigh correlation
MCV is highly overall correlated with HGB and 1 other fieldsHigh correlation
NEUTn is highly overall correlated with NEUTp and 1 other fieldsHigh correlation
NEUTp is highly overall correlated with HCT and 2 other fieldsHigh correlation
PCT is highly overall correlated with HCT and 2 other fieldsHigh correlation
PLT is highly overall correlated with PCTHigh correlation
RBC is highly overall correlated with HGBHigh correlation
WBC is highly overall correlated with NEUTnHigh correlation
NEUTp is highly skewed (γ1 = 34.94649487)Skewed
LYMn is highly skewed (γ1 = 22.11416132)Skewed
RBC is highly skewed (γ1 = 24.57363728)Skewed
HCT is highly skewed (γ1 = 33.72923959)Skewed
MCV is highly skewed (γ1 = 28.59593292)Skewed
MCH is highly skewed (γ1 = 25.40825318)Skewed

Reproduction

Analysis started2024-10-02 10:15:02.462754
Analysis finished2024-10-02 10:15:25.338693
Duration22.88 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

WBC
Real number (ℝ)

HIGH CORRELATION 

Distinct278
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8627166
Minimum0.8
Maximum45.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-02T12:15:25.428431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile3.92
Q16
median7.4
Q38.68
95-th percentile13.41
Maximum45.7
Range44.9
Interquartile range (IQR)2.68

Descriptive statistics

Standard deviation3.5644657
Coefficient of variation (CV)0.45333768
Kurtosis27.922299
Mean7.8627166
Median Absolute Deviation (MAD)1.3
Skewness3.807032
Sum10072.14
Variance12.705416
MonotonicityNot monotonic
2024-10-02T12:15:25.553324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.4 44
 
3.4%
7.2 41
 
3.2%
7 39
 
3.0%
8.3 38
 
3.0%
7.6 37
 
2.9%
7.8 33
 
2.6%
8 30
 
2.3%
8.7 27
 
2.1%
7.1 27
 
2.1%
9 25
 
2.0%
Other values (268) 940
73.4%
ValueCountFrequency (%)
0.8 1
 
0.1%
2 3
0.2%
2.4 5
0.4%
2.5 1
 
0.1%
2.6 3
0.2%
2.7 5
0.4%
2.8 4
0.3%
2.88 2
 
0.2%
2.9 3
0.2%
3 2
 
0.2%
ValueCountFrequency (%)
45.7 1
0.1%
42.42 1
0.1%
41.9 1
0.1%
32.72 1
0.1%
28.03 1
0.1%
26.95 1
0.1%
24.4 1
0.1%
23.57 1
0.1%
23.36 1
0.1%
23.1 1
0.1%

LYMp
Real number (ℝ)

Distinct263
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.845
Minimum6.2
Maximum91.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-02T12:15:25.678207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile13.1
Q125.845
median25.845
Q325.845
95-th percentile39
Maximum91.4
Range85.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.0387276
Coefficient of variation (CV)0.27234388
Kurtosis13.185716
Mean25.845
Median Absolute Deviation (MAD)0
Skewness1.6565492
Sum33107.445
Variance49.543686
MonotonicityNot monotonic
2024-10-02T12:15:25.799778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.845 781
61.0%
35.6 7
 
0.5%
27.8 7
 
0.5%
18.9 6
 
0.5%
43.4 6
 
0.5%
16.1 5
 
0.4%
30.7 5
 
0.4%
19 5
 
0.4%
27.7 5
 
0.4%
15.6 5
 
0.4%
Other values (253) 449
35.1%
ValueCountFrequency (%)
6.2 1
 
0.1%
6.9 1
 
0.1%
7.3 3
0.2%
7.8 3
0.2%
8.1 1
 
0.1%
8.5 1
 
0.1%
8.9 2
0.2%
9.2 1
 
0.1%
9.3 1
 
0.1%
9.5 3
0.2%
ValueCountFrequency (%)
91.4 1
0.1%
89.8 1
0.1%
59.2 1
0.1%
55.1 1
0.1%
54.9 1
0.1%
54.5 1
0.1%
52.1 1
0.1%
51.5 1
0.1%
49 1
0.1%
48.5 1
0.1%

NEUTp
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct267
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.511
Minimum0.7
Maximum5317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-02T12:15:25.920290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile53.7
Q171.1
median77.511
Q377.511
95-th percentile78.7
Maximum5317
Range5316.3
Interquartile range (IQR)6.411

Descriptive statistics

Standard deviation147.74627
Coefficient of variation (CV)1.906133
Kurtosis1238.608
Mean77.511
Median Absolute Deviation (MAD)0
Skewness34.946495
Sum99291.591
Variance21828.961
MonotonicityNot monotonic
2024-10-02T12:15:26.037201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.511 781
61.0%
74.3 8
 
0.6%
63.5 6
 
0.5%
72.5 6
 
0.5%
71.5 6
 
0.5%
58.4 5
 
0.4%
61.3 5
 
0.4%
64.9 5
 
0.4%
78.4 5
 
0.4%
56.3 5
 
0.4%
Other values (257) 449
35.1%
ValueCountFrequency (%)
0.7 1
0.1%
1.2 1
0.1%
7.6 1
0.1%
9.7 1
0.1%
30.4 1
0.1%
31.8 1
0.1%
34.8 1
0.1%
37.9 1
0.1%
38.2 1
0.1%
39.3 1
0.1%
ValueCountFrequency (%)
5317 1
 
0.1%
671 1
 
0.1%
86.1 2
0.2%
85.8 3
0.2%
85.3 1
 
0.1%
85.1 1
 
0.1%
84.7 3
0.2%
84.6 1
 
0.1%
84.2 1
 
0.1%
83.6 1
 
0.1%

LYMn
Real number (ℝ)

SKEWED 

Distinct51
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.88076
Minimum0.2
Maximum41.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-02T12:15:26.170453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.9
Q11.88076
median1.88076
Q31.88076
95-th percentile2.6
Maximum41.8
Range41.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.335689
Coefficient of variation (CV)0.71018577
Kurtosis633.18822
Mean1.88076
Median Absolute Deviation (MAD)0
Skewness22.114161
Sum2409.2536
Variance1.7840651
MonotonicityNot monotonic
2024-10-02T12:15:26.304368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.88076 781
61.0%
1.5 38
 
3.0%
1.6 35
 
2.7%
2 30
 
2.3%
1.9 29
 
2.3%
1 28
 
2.2%
1.7 28
 
2.2%
1.4 26
 
2.0%
0.6 26
 
2.0%
1.2 23
 
1.8%
Other values (41) 237
 
18.5%
ValueCountFrequency (%)
0.2 2
 
0.2%
0.4 4
 
0.3%
0.5 4
 
0.3%
0.6 26
2.0%
0.7 5
 
0.4%
0.8 19
1.5%
0.9 11
 
0.9%
1 28
2.2%
1.1 19
1.5%
1.18 1
 
0.1%
ValueCountFrequency (%)
41.8 1
0.1%
14 1
0.1%
13.2 1
0.1%
6.3 1
0.1%
6.1 1
0.1%
5.8 1
0.1%
5.6 2
0.2%
5.4 1
0.1%
5.1 1
0.1%
5 1
0.1%

NEUTn
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.14094
Minimum0.5
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-02T12:15:26.437248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile2.5
Q15.1
median5.14094
Q35.14094
95-th percentile7.8
Maximum79
Range78.5
Interquartile range (IQR)0.04094

Descriptive statistics

Standard deviation2.8722937
Coefficient of variation (CV)0.55870982
Kurtosis368.68605
Mean5.14094
Median Absolute Deviation (MAD)0
Skewness15.723494
Sum6585.5441
Variance8.250071
MonotonicityNot monotonic
2024-10-02T12:15:26.588019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.14094 781
61.0%
3.6 20
 
1.6%
5 16
 
1.2%
4.6 14
 
1.1%
4.3 14
 
1.1%
5.3 14
 
1.1%
2.5 13
 
1.0%
4.4 12
 
0.9%
3.4 12
 
0.9%
2.3 12
 
0.9%
Other values (93) 373
29.1%
ValueCountFrequency (%)
0.5 2
 
0.2%
1.2 1
 
0.1%
1.3 6
0.5%
1.4 4
0.3%
1.5 1
 
0.1%
1.6 4
0.3%
1.7 2
 
0.2%
1.8 5
0.4%
1.9 5
0.4%
2 3
0.2%
ValueCountFrequency (%)
79 1
0.1%
44 1
0.1%
19.9 1
0.1%
17.9 1
0.1%
17.4 1
0.1%
17 2
0.2%
14.2 1
0.1%
13.7 1
0.1%
12.9 1
0.1%
12.5 2
0.2%

RBC
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct272
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.708267
Minimum1.36
Maximum90.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-02T12:15:26.736681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile3.34
Q14.19
median4.6
Q35.1
95-th percentile5.7
Maximum90.8
Range89.44
Interquartile range (IQR)0.91

Descriptive statistics

Standard deviation2.8172004
Coefficient of variation (CV)0.59835188
Kurtosis708.35308
Mean4.708267
Median Absolute Deviation (MAD)0.48
Skewness24.573637
Sum6031.29
Variance7.9366181
MonotonicityNot monotonic
2024-10-02T12:15:26.861805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.2 42
 
3.3%
4.4 35
 
2.7%
4.7 33
 
2.6%
5.3 30
 
2.3%
5.5 30
 
2.3%
4.8 28
 
2.2%
5.7 26
 
2.0%
4.6 26
 
2.0%
5.4 25
 
2.0%
4.2 25
 
2.0%
Other values (262) 981
76.6%
ValueCountFrequency (%)
1.36 1
 
0.1%
1.42 1
 
0.1%
1.91 2
0.2%
1.92 1
 
0.1%
1.96 1
 
0.1%
2.17 1
 
0.1%
2.25 1
 
0.1%
2.26 4
0.3%
2.37 2
0.2%
2.53 1
 
0.1%
ValueCountFrequency (%)
90.8 1
0.1%
41.1 1
0.1%
29.7 1
0.1%
13.1 1
0.1%
10.9 1
0.1%
6.9 1
0.1%
6.67 2
0.2%
6.6 1
0.1%
6.58 1
0.1%
6.05 2
0.2%

HGB
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.184551
Minimum-10
Maximum87.1
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size10.1 KiB
2024-10-02T12:15:26.996573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile8.4
Q110.8
median12.3
Q313.5
95-th percentile15
Maximum87.1
Range97.1
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation3.8128972
Coefficient of variation (CV)0.31292882
Kurtosis223.95444
Mean12.184551
Median Absolute Deviation (MAD)1.4
Skewness11.578004
Sum15608.41
Variance14.538185
MonotonicityNot monotonic
2024-10-02T12:15:27.126279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 37
 
2.9%
14.2 34
 
2.7%
13.2 33
 
2.6%
12.5 32
 
2.5%
13 31
 
2.4%
11.8 30
 
2.3%
12.6 30
 
2.3%
13.4 29
 
2.3%
12.3 28
 
2.2%
11.9 28
 
2.2%
Other values (106) 969
75.6%
ValueCountFrequency (%)
-10 1
0.1%
0.4 1
0.1%
1.2 1
0.1%
1.8 1
0.1%
3.1 1
0.1%
3.8 1
0.1%
4.2 2
0.2%
4.82 1
0.1%
5 2
0.2%
5.2 1
0.1%
ValueCountFrequency (%)
87.1 1
0.1%
85.1 1
0.1%
41 1
0.1%
35.2 1
0.1%
19.6 1
0.1%
17.5 1
0.1%
16.7 1
0.1%
16.5 2
0.2%
16.2 1
0.1%
16 2
0.2%

HCT
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct206
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.1526
Minimum2
Maximum3715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-02T12:15:27.254291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile30.3
Q139.2
median46.1526
Q346.1526
95-th percentile46.1526
Maximum3715
Range3713
Interquartile range (IQR)6.9526

Descriptive statistics

Standard deviation104.8861
Coefficient of variation (CV)2.2725935
Kurtosis1173.6334
Mean46.1526
Median Absolute Deviation (MAD)0
Skewness33.72924
Sum59121.481
Variance11001.094
MonotonicityNot monotonic
2024-10-02T12:15:27.386283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.1526 781
61.0%
36.5 7
 
0.5%
39.4 7
 
0.5%
34.7 7
 
0.5%
34.3 7
 
0.5%
37.2 7
 
0.5%
34.8 6
 
0.5%
37.6 6
 
0.5%
36.2 6
 
0.5%
32.1 6
 
0.5%
Other values (196) 441
34.4%
ValueCountFrequency (%)
2 1
0.1%
10.1 1
0.1%
12.1 1
0.1%
14 1
0.1%
14.9 1
0.1%
17 1
0.1%
19.5 1
0.1%
19.9 1
0.1%
20.1 1
0.1%
20.2 1
0.1%
ValueCountFrequency (%)
3715 1
0.1%
741 1
0.1%
316 1
0.1%
81.2 1
0.1%
53.3 1
0.1%
50.4 1
0.1%
50.2 2
0.2%
49.8 1
0.1%
49.5 2
0.2%
49.3 1
0.1%

MCV
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct317
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.793919
Minimum-79.3
Maximum990
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size10.1 KiB
2024-10-02T12:15:27.521021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-79.3
5-th percentile68.8
Q181.2
median86.6
Q390.2
95-th percentile96
Maximum990
Range1069.3
Interquartile range (IQR)9

Descriptive statistics

Standard deviation27.177663
Coefficient of variation (CV)0.31677844
Kurtosis959.88288
Mean85.793919
Median Absolute Deviation (MAD)4.4
Skewness28.595933
Sum109902.01
Variance738.62539
MonotonicityNot monotonic
2024-10-02T12:15:27.650021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 57
 
4.4%
88 53
 
4.1%
87 44
 
3.4%
90 40
 
3.1%
93 34
 
2.7%
92 33
 
2.6%
86 29
 
2.3%
84 26
 
2.0%
95 24
 
1.9%
96 22
 
1.7%
Other values (307) 919
71.7%
ValueCountFrequency (%)
-79.3 1
0.1%
25.1 1
0.1%
30.3 1
0.1%
31.3 1
0.1%
36.6 1
0.1%
44.9 1
0.1%
46.6 1
0.1%
55.7 1
0.1%
56.1 1
0.1%
57.3 1
0.1%
ValueCountFrequency (%)
990 1
0.1%
124.1 1
0.1%
122.1 1
0.1%
119 2
0.2%
117.3 1
0.1%
113.8 1
0.1%
112.8 1
0.1%
111.2 1
0.1%
110.9 1
0.1%
109 1
0.1%

MCH
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct191
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.08484
Minimum10.9
Maximum3117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-02T12:15:28.182991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.9
5-th percentile20.2
Q125.5
median27.8
Q329.6
95-th percentile32
Maximum3117
Range3106.1
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation111.17076
Coefficient of variation (CV)3.4648998
Kurtosis656.48004
Mean32.08484
Median Absolute Deviation (MAD)2.1
Skewness25.408253
Sum41100.68
Variance12358.937
MonotonicityNot monotonic
2024-10-02T12:15:28.316991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 119
 
9.3%
30 84
 
6.6%
28 77
 
6.0%
31 74
 
5.8%
27 52
 
4.1%
26 35
 
2.7%
25 23
 
1.8%
32 21
 
1.6%
28.8 17
 
1.3%
26.8 17
 
1.3%
Other values (181) 762
59.5%
ValueCountFrequency (%)
10.9 1
0.1%
11.4 1
0.1%
14.2 1
0.1%
14.7 1
0.1%
15.6 1
0.1%
15.8 1
0.1%
15.9 1
0.1%
16.7 1
0.1%
16.8 1
0.1%
17 1
0.1%
ValueCountFrequency (%)
3117 1
0.1%
2511 1
0.1%
275 1
0.1%
241 1
0.1%
90 1
0.1%
88.2 1
0.1%
76 1
0.1%
41.4 1
0.1%
41.2 2
0.2%
40.1 1
0.1%

MCHC
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.739149
Minimum11.5
Maximum92.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-02T12:15:28.443991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.5
5-th percentile28.5
Q130.6
median32
Q332.9
95-th percentile34.1
Maximum92.8
Range81.3
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation3.300352
Coefficient of variation (CV)0.10398363
Kurtosis137.82462
Mean31.739149
Median Absolute Deviation (MAD)1
Skewness8.2437228
Sum40657.85
Variance10.892323
MonotonicityNot monotonic
2024-10-02T12:15:28.576991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 261
20.4%
33 182
 
14.2%
31.8 33
 
2.6%
31.4 27
 
2.1%
31 26
 
2.0%
30.2 21
 
1.6%
30.3 21
 
1.6%
30.6 20
 
1.6%
31.1 20
 
1.6%
30.1 20
 
1.6%
Other values (116) 650
50.7%
ValueCountFrequency (%)
11.5 1
0.1%
12.4 1
0.1%
20.2 1
0.1%
20.3 1
0.1%
22.9 1
0.1%
23.6 1
0.1%
24.1 1
0.1%
24.5 1
0.1%
25 1
0.1%
25.1 1
0.1%
ValueCountFrequency (%)
92.8 1
0.1%
79.6 1
0.1%
61.6 1
0.1%
61.3 1
0.1%
50.2 1
0.1%
42 1
0.1%
41 1
0.1%
40.2 1
0.1%
39.6 1
0.1%
39.4 2
0.2%

PLT
Real number (ℝ)

HIGH CORRELATION 

Distinct291
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.98142
Minimum10
Maximum660
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-02T12:15:28.711991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile102
Q1157
median213
Q3293
95-th percentile380
Maximum660
Range650
Interquartile range (IQR)136

Descriptive statistics

Standard deviation93.019336
Coefficient of variation (CV)0.40446457
Kurtosis-0.03416864
Mean229.98142
Median Absolute Deviation (MAD)63
Skewness0.44635689
Sum294606.2
Variance8652.5968
MonotonicityNot monotonic
2024-10-02T12:15:28.837079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360 41
 
3.2%
280 36
 
2.8%
260 30
 
2.3%
380 29
 
2.3%
350 27
 
2.1%
290 27
 
2.1%
370 25
 
2.0%
330 25
 
2.0%
340 25
 
2.0%
310 21
 
1.6%
Other values (281) 995
77.7%
ValueCountFrequency (%)
10 1
0.1%
11.3 1
0.1%
11.4 1
0.1%
11.8 1
0.1%
12.4 1
0.1%
13.9 1
0.1%
17 2
0.2%
24 1
0.1%
32 2
0.2%
35 1
0.1%
ValueCountFrequency (%)
660 1
0.1%
589 1
0.1%
534 2
0.2%
532 1
0.1%
510 1
0.1%
508 1
0.1%
499 1
0.1%
497 1
0.1%
461 1
0.1%
457 1
0.1%

PDW
Real number (ℝ)

Distinct104
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.312512
Minimum8.4
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-02T12:15:28.964440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile11.3
Q113.3
median14.312512
Q314.7
95-th percentile17.4
Maximum97
Range88.6
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation3.0050786
Coefficient of variation (CV)0.20996166
Kurtosis448.52621
Mean14.312512
Median Absolute Deviation (MAD)0.71251157
Skewness16.599189
Sum18334.327
Variance9.0304975
MonotonicityNot monotonic
2024-10-02T12:15:29.092440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.31251157 417
32.6%
13.1 40
 
3.1%
13.6 39
 
3.0%
12.8 36
 
2.8%
13.3 35
 
2.7%
14.6 31
 
2.4%
12.5 30
 
2.3%
13.8 28
 
2.2%
14.3 27
 
2.1%
12.3 24
 
1.9%
Other values (94) 574
44.8%
ValueCountFrequency (%)
8.4 2
 
0.2%
8.6 1
 
0.1%
8.9 1
 
0.1%
9.2 1
 
0.1%
9.3 1
 
0.1%
9.5 2
 
0.2%
9.7 3
0.2%
9.8 1
 
0.1%
10 5
0.4%
10.2 6
0.5%
ValueCountFrequency (%)
97 1
0.1%
29.2 1
0.1%
25 2
0.2%
24.6 1
0.1%
22.8 2
0.2%
22.5 1
0.1%
22.1 1
0.1%
21.8 2
0.2%
21.3 2
0.2%
21.2 1
0.1%

PCT
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26028
Minimum0.01
Maximum13.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2024-10-02T12:15:29.213440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.11
Q10.17
median0.26028
Q30.26028
95-th percentile0.26028
Maximum13.6
Range13.59
Interquartile range (IQR)0.09028

Descriptive statistics

Standard deviation0.6853506
Coefficient of variation (CV)2.6331282
Kurtosis318.0429
Mean0.26028
Median Absolute Deviation (MAD)0
Skewness17.764033
Sum333.41868
Variance0.46970544
MonotonicityNot monotonic
2024-10-02T12:15:29.343440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.26028 781
61.0%
0.15 53
 
4.1%
0.12 49
 
3.8%
0.17 45
 
3.5%
0.13 44
 
3.4%
0.16 43
 
3.4%
0.18 35
 
2.7%
0.14 33
 
2.6%
0.11 24
 
1.9%
0.22 23
 
1.8%
Other values (30) 151
 
11.8%
ValueCountFrequency (%)
0.01 2
 
0.2%
0.03 1
 
0.1%
0.04 1
 
0.1%
0.05 2
 
0.2%
0.07 6
 
0.5%
0.08 10
 
0.8%
0.09 9
 
0.7%
0.1 17
 
1.3%
0.11 24
1.9%
0.12 49
3.8%
ValueCountFrequency (%)
13.6 1
 
0.1%
12.8 1
 
0.1%
12 1
 
0.1%
11.3 1
 
0.1%
0.45 1
 
0.1%
0.44 1
 
0.1%
0.41 1
 
0.1%
0.36 1
 
0.1%
0.35 4
0.3%
0.34 1
 
0.1%

Diagnosis
Categorical

Distinct9
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Healthy
336 
Normocytic hypochromic anemia
279 
Normocytic normochromic anemia
269 
Iron deficiency anemia
189 
Thrombocytopenia
73 
Other values (4)
135 

Length

Max length30
Median length23
Mean length20.459016
Min length7

Characters and Unicode

Total characters26208
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormocytic hypochromic anemia
2nd rowNormocytic hypochromic anemia
3rd rowIron deficiency anemia
4th rowIron deficiency anemia
5th rowNormocytic hypochromic anemia

Common Values

ValueCountFrequency (%)
Healthy 336
26.2%
Normocytic hypochromic anemia 279
21.8%
Normocytic normochromic anemia 269
21.0%
Iron deficiency anemia 189
14.8%
Thrombocytopenia 73
 
5.7%
Other microcytic anemia 59
 
4.6%
Leukemia 47
 
3.7%
Macrocytic anemia 18
 
1.4%
Leukemia with thrombocytopenia 11
 
0.9%

Length

2024-10-02T12:15:29.462440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-02T12:15:29.575441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
anemia 814
27.9%
normocytic 548
18.8%
healthy 336
11.5%
hypochromic 279
 
9.6%
normochromic 269
 
9.2%
iron 189
 
6.5%
deficiency 189
 
6.5%
thrombocytopenia 84
 
2.9%
other 59
 
2.0%
microcytic 59
 
2.0%
Other values (3) 87
 
3.0%

Most occurring characters

ValueCountFrequency (%)
o 2979
11.4%
c 2885
11.0%
i 2577
9.8%
m 2380
9.1%
a 2124
8.1%
e 1787
 
6.8%
r 1774
 
6.8%
1632
 
6.2%
n 1545
 
5.9%
y 1513
 
5.8%
Other values (17) 5012
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26208
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2979
11.4%
c 2885
11.0%
i 2577
9.8%
m 2380
9.1%
a 2124
8.1%
e 1787
 
6.8%
r 1774
 
6.8%
1632
 
6.2%
n 1545
 
5.9%
y 1513
 
5.8%
Other values (17) 5012
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26208
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2979
11.4%
c 2885
11.0%
i 2577
9.8%
m 2380
9.1%
a 2124
8.1%
e 1787
 
6.8%
r 1774
 
6.8%
1632
 
6.2%
n 1545
 
5.9%
y 1513
 
5.8%
Other values (17) 5012
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26208
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2979
11.4%
c 2885
11.0%
i 2577
9.8%
m 2380
9.1%
a 2124
8.1%
e 1787
 
6.8%
r 1774
 
6.8%
1632
 
6.2%
n 1545
 
5.9%
y 1513
 
5.8%
Other values (17) 5012
19.1%

Interactions

2024-10-02T12:15:23.739870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:03.020799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:04.503182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:05.837709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:07.253574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:08.637951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:10.132263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:13.983348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:15.320643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:16.763909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:18.420537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:19.679541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:20.977330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:22.533073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:23.828883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:03.138798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:04.598176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:05.940676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:07.344576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:08.736627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:10.224266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:14.069300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:15.415621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:16.857906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:18.502351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:19.764552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:21.062330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:22.615073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:23.916883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:03.246801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:04.687176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:06.032577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:07.430574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:08.830630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:10.315261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:14.169224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:15.512632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:16.941104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:18.595472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:19.847553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:21.143319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:22.693073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:24.010926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:03.350801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:04.781174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:06.119740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:07.524573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:08.936874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:12.879895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:14.252968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:15.617632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:17.305408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:18.683825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:19.943541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:21.229336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:22.775073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:24.102921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:03.445495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:04.869853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:06.220425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:07.614573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:09.039549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:13.003456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:14.337126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:15.715632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:17.407687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:18.770301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:20.031552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:21.315328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:22.854743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:24.206620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:03.561495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:04.970412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:06.342501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:07.720282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:09.163549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:13.103771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:14.453977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:15.825606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:17.513885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:18.860445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:20.131552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:21.412330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:22.949458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:24.298610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:03.659494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:05.063956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:06.427271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:07.810274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:09.264550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:13.203028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:14.537320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:15.926600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:17.606984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:18.956398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:20.219552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:21.496351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:23.030452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:24.385620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:03.759444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:05.142381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:06.531627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:07.901272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:09.365463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:13.301332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:14.630130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:16.023581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:17.711165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:19.039398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:20.311552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:21.581344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:23.109461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:24.480888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:03.873439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:05.242766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:06.636932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:08.014273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:09.475462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:13.401659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:14.718573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:16.130594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:17.820147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:19.138398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:20.410578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:21.674342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:23.200469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:24.567888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:03.986444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:05.347218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:06.727256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:08.110281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:09.579464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:13.495584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:14.818074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:16.233325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:17.902387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:19.224437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:20.501572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:21.760351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:23.284477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:24.662567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:04.095138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:05.443104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:06.837065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:08.215284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:09.686350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:13.591946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:14.921415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:16.340314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:18.009979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:19.315437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:20.596578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:21.848398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:23.374138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:24.762578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:04.203139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:05.541595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:06.949927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:08.328284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:09.805349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:13.684819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:15.025404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:16.451625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:18.118155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:19.412437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:20.694673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:21.946359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:23.475157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:24.855570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:04.307139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:05.640213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:07.055574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:08.435951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:09.915266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:13.795819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:15.123417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:16.560193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:18.218075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:19.503429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:20.794674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:22.355375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:23.563135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:24.937392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:04.399182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:05.738087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:07.148576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:08.531952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:10.019267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:13.884949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:15.215416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:16.658919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:18.303654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:19.586544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:20.881005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:22.440073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-02T12:15:23.646880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-10-02T12:15:29.691104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DiagnosisHCTHGBLYMnLYMpMCHMCHCMCVNEUTnNEUTpPCTPDWPLTRBCWBC
Diagnosis1.0000.0000.1980.0230.1780.0000.1360.0650.2200.0000.1130.0440.3120.0660.238
HCT0.0001.0000.4430.212-0.0120.5180.4440.4610.3090.5640.7150.3220.4350.1970.200
HGB0.1980.4431.0000.039-0.0390.5850.5200.5300.1330.1910.1340.1310.2370.7490.195
LYMn0.0230.2120.0391.0000.4350.0640.0120.0500.184-0.1670.3090.0920.2630.0390.297
LYMp0.178-0.012-0.0390.4351.000-0.036-0.044-0.040-0.479-0.4580.0660.0090.079-0.003-0.242
MCH0.0000.5180.5850.064-0.0361.0000.7260.8310.1740.3870.3840.1920.3130.1680.146
MCHC0.1360.4440.5200.012-0.0440.7261.0000.4450.1520.3350.2920.1490.2190.1500.068
MCV0.0650.4610.5300.050-0.0400.8310.4451.0000.1470.3330.3230.1630.3240.1660.201
NEUTn0.2200.3090.1330.184-0.4790.1740.1520.1471.0000.5070.2930.0850.2060.0460.609
NEUTp0.0000.5640.191-0.167-0.4580.3870.3350.3330.5071.0000.5290.1870.2980.0160.288
PCT0.1130.7150.1340.3090.0660.3840.2920.3230.2930.5291.0000.3230.6340.0110.237
PDW0.0440.3220.1310.0920.0090.1920.1490.1630.0850.1870.3231.0000.1950.0470.046
PLT0.3120.4350.2370.2630.0790.3130.2190.3240.2060.2980.6340.1951.0000.2800.371
RBC0.0660.1970.7490.039-0.0030.1680.1500.1660.0460.0160.0110.0470.2801.0000.219
WBC0.2380.2000.1950.297-0.2420.1460.0680.2010.6090.2880.2370.0460.3710.2191.000

Missing values

2024-10-02T12:15:25.078203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-02T12:15:25.255935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

WBCLYMpNEUTpLYMnNEUTnRBCHGBHCTMCVMCHMCHCPLTPDWPCTDiagnosis
010.043.250.14.35.02.777.324.287.726.330.1189.012.50.17Normocytic hypochromic anemia
110.042.452.34.25.32.847.325.088.225.720.2180.012.50.16Normocytic hypochromic anemia
27.230.760.72.24.43.979.030.577.022.629.5148.014.30.14Iron deficiency anemia
36.030.263.51.83.84.223.832.877.923.229.8143.011.30.12Iron deficiency anemia
44.239.153.71.62.33.930.4316.080.623.929.7236.012.80.22Normocytic hypochromic anemia
56.627.365.41.84.33.968.829.775.222.279.6207.011.50.18Other microcytic anemia
616.719.168.23.211.45.1514.244.887.127.531.6151.012.80.14Leukemia
79.327.464.02.65.94.3912.037.986.427.331.6194.015.90.19Normocytic hypochromic anemia
85.219.772.41.03.84.8513.241.084.727.232.1181.010.00.15Healthy
910.512.479.01.38.34.5712.438.985.327.131.8164.011.30.14Normocytic hypochromic anemia
WBCLYMpNEUTpLYMnNEUTnRBCHGBHCTMCVMCHMCHCPLTPDWPCTDiagnosis
12717.2025.84577.5111.880765.140944.7614.446.152690.130.233.5244.014.3125120.26028Healthy
12726.7025.84577.5111.880765.140944.0012.946.152693.732.334.5312.014.3125120.26028Normocytic normochromic anemia
12738.7025.84577.5111.880765.140944.6612.246.152676.626.234.1266.014.3125120.26028Other microcytic anemia
12747.7025.84577.5111.880765.140943.429.846.152689.728.732.0457.014.3125120.26028Normocytic normochromic anemia
12756.7025.84577.5111.880765.140944.659.546.152662.620.432.6200.014.3125120.26028Other microcytic anemia
12764.4025.84577.5111.880765.140944.8613.546.152680.727.734.4180.014.3125120.26028Healthy
12775.6025.84577.5111.880765.140944.8515.046.152691.731.033.8215.014.3125120.26028Healthy
12789.2025.84577.5111.880765.140944.4713.146.152688.729.333.0329.014.3125120.26028Healthy
12796.4825.84577.5111.880765.140944.7513.246.152686.727.932.1174.014.3125120.26028Healthy
12808.8025.84577.5111.880765.140944.9515.246.152689.730.634.2279.014.3125120.26028Healthy

Duplicate rows

Most frequently occurring

WBCLYMpNEUTpLYMnNEUTnRBCHGBHCTMCVMCHMCHCPLTPDWPCTDiagnosis# duplicates
298.3025.84577.5111.880765.140945.2014.246.152691.030.033.0360.014.3125120.26028Healthy4
157.3025.84577.5111.880765.140944.7012.546.152687.029.032.0270.014.3125120.26028Normocytic normochromic anemia3
177.4025.84577.5111.880765.140944.8012.646.152686.029.033.0280.014.3000000.26028Normocytic normochromic anemia3
348.707.30085.8000.600007.500004.4412.939.600089.329.032.5140.011.3000000.12000Normocytic normochromic anemia3
359.0025.84577.5111.880765.140945.8015.046.152696.032.033.0400.014.3125120.26028Healthy3
02.8825.84577.5111.880765.140943.019.046.1526103.729.928.8400.014.3125120.26028Macrocytic anemia2
13.1020.30072.1000.600002.300005.3612.240.400075.522.730.1149.013.6000000.14000Iron deficiency anemia2
23.2035.60056.2001.100001.800005.2214.945.800087.828.532.5132.014.3000000.13000Thrombocytopenia2
33.9225.84577.5111.880765.140944.9713.946.152688.128.031.7229.014.3125120.26028Healthy2
44.7027.80062.3001.300002.900004.3812.338.400087.828.032.0171.011.5000000.15000Normocytic normochromic anemia2